# -*- coding:utf-8 -*-
# -*- coding:utf-8 -*-
# ==============================================================================
# 20171115
# HelloZEX
# 卷积神经网络 实现手写数字识别
# 生成并保存模型
# ==============================================================================

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data", one_hot=True)


import tensorflow as tf

sess = tf.InteractiveSession()


x = tf.placeholder(tf.float32, shape=[None, 784])
y_ = tf.placeholder(tf.float32, shape=[None, 10])
W = tf.Variable(tf.zeros([784 ,10]))
b = tf.Variable(tf.zeros([10]))


sess.run(tf.global_variables_initializer())

y = tf.matmul(x ,W) + b

cross_entropy = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))

train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

for _ in range(1000):
    batch = mnist.train.next_batch(100)
    train_step.run(feed_dict={x: batch[0], y_: batch[1]})
correct_prediction = tf.equal(tf.argmax(y ,1), tf.argmax(y_ ,1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

print(accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels}))

def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)

def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)

def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                          strides=[1, 2, 2, 1], padding='SAME')

W_conv1 = weight_variable([5, 5, 1, 64])
b_conv1 = bias_variable([64])
x_image = tf.reshape(x, [-1 ,28 ,28 ,1])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

W_conv2 = weight_variable([5, 5, 64, 128])
b_conv2 = bias_variable([128])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)

w_conv3=weight_variable([3,3,128,256])
b_conv3=bias_variable([256])
h_conv3=tf.nn.relu(conv2d(h_pool2,w_conv3)+b_conv3)

W_fc1 = weight_variable([7 * 7 * 256, 1024])
b_fc1 = bias_variable([1024])

h_pool2_flat = tf.reshape(h_conv3, [-1, 7* 7 * 256])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])

y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
cross_entropy = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

saver = tf.train.Saver()  # defaults to saving all variables

sess.run(tf.global_variables_initializer())
for i in range(1000):
    batch = mnist.train.next_batch(50)
    if i % 100 == 0:
        train_accuracy = accuracy.eval(feed_dict={
            x: batch[0], y_: batch[1], keep_prob: 1.0})
        print(batch[0].shape)
        print(batch[1].shape)
        print("step %d, training accuracy %g" % (i, train_accuracy))

    train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
# 保存模型参数，注意把这里改为自己的路径
saver.save(sess, 'CKPT/model.ckpt')

print("test accuracy %g" % accuracy.eval(feed_dict={
    x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))

print("Finish!")